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Machine Learning Approach for Classifying Malicious URLs
Preeti,  Saurabh Mittal
The discrimination of ordinary and maliciousactivity of program by monitoring HTTP visitors is fitting more difficult when sophisticated malware generate authorized HTTP traffic and having the identical habits with ordinary application. In this paper, a brand new strategy is proposed to support administrator in detection of malicious clients by using clustering customers into businesses founded on HTTP-pastime similarity. Size of the auto-application activity in the network can be can also be noticeable from the influence of the system. Nevertheless, the approach wishes to be increased for mechanically discover malicious agencies without using blacklist or outcomes of alternative malicious detection methods. One more terrible point of this system is that with a malware has simply been infected in just one client, that malware's conduct are not able to be detected.Also the identical time, the normal supervised learning algorithms are recognized to generalize well over the certain patterns determined in coaching knowledge, which makes them a greater replacement towards hacking campaigns. Nevertheless, the particularly dynamic environment of those campaigns requires updating the items often, and this poses new challenges in view that many of the traditional learning algorithms are also computationally high-priced to retrain. This paper compares desktop learning techniques (OneR, ZeroR, and Random woodland) for detecting malicious webpages.
Keywords- Computer Security, Adware Classification, Machine Learning, Random Forest
Unique Identification Number - IJEDR1604091Page Number(s) - 622-629Pubished in - Volume 4 | Issue 4 | December 2016DOI (Digital Object Identifier) -    Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Preeti,  Saurabh Mittal,   "Machine Learning Approach for Classifying Malicious URLs"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.4, Issue 4, pp.622-629, December 2016, Available at :http://www.ijedr.org/papers/IJEDR1604091.pdf